HW5 - Make Data Move
05/10/2020
Explore the recent global developments with R
Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
Get the necessary packages
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
Look at the data
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
# call the gapminder data "gapminder"
data <- gapminder
unique(data$year)## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007head(data)## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(data, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() 
We see an interesting spread with an outlier to the right. Answer the following questions, please:
Q1. Why does it make sense to have a log10 scale on x axis?
This is because gdp per capita grows exponentially, and by using a log transformation on the data, it will seem linear, which makes it easier to compare the differences in gdp per capita between countries or years.
Q2. What country is the richest in 1952 (far right on x axis)?
To answer this, I execute the following command:
data %>% subset(year == 1952) %>% arrange(desc(gdpPercap)) %>% head()## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Kuwait Asia 1952 55.6 160000 108382.
## 2 Switzerland Europe 1952 69.6 4815000 14734.
## 3 United States Americas 1952 68.4 157553000 13990.
## 4 Canada Americas 1952 68.8 14785584 11367.
## 5 New Zealand Oceania 1952 69.4 1994794 10557.
## 6 Norway Europe 1952 72.7 3327728 10095.I first take the gapminder dataset which I have called “data”, use pipes to subset the year 1952, use pipes again to arrange it in descending order based on the column gdpPercap. This I pipe again to use the head() function to get the five top rows. In the first row is Kuwait with a gdp per capita of 108382, which thereby seems to be the richest country in 1952. However, as it is so much richer than the second richest coutntry (Switzerland with a gdp per capita of 14734), I wonder if Kuwait’s score is a mistake in the dataset (as it is mentioned that there is an outlier).
You can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Q3. Can you differentiate the continents by color and fix the axis labels?
To do this, I add aesthetics to geom_point and ask R to color the points based on continent. I also add axis labels using the labs() function, in which I define the text for the title, legends and x and y axes of the graph:
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point(aes(color = continent)) +
scale_x_log10() +
labs(title = "Gdp per capita in 2007, colour-coded by continent",
x = "Gdp per capita",
y = "Life expectancy",
size = "Population",
color = "Continent") +
scale_size_continuous(labels = function(x) format(x, scientific = FALSE))
Q4. What are the five richest countries in the world in 2007?
To find out, I use the same code as for Q2, but set year to 2007 instead of 1952. I also tell the head() function to take only the five first entries:
data %>% subset(year == 2007) %>% arrange(desc(gdpPercap)) %>% head(5)## # A tibble: 5 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Norway Europe 2007 80.2 4627926 49357.
## 2 Kuwait Asia 2007 77.6 2505559 47307.
## 3 Singapore Asia 2007 80.0 4553009 47143.
## 4 United States Americas 2007 78.2 301139947 42952.
## 5 Ireland Europe 2007 78.9 4109086 40676.I see that Norway, Kuwait, Singapore, United States and Ireland (in this order) are the five richest countries in 2007.
Make it move!
The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. And there are two ways of animating the gapminder ggplot.
Option 1: Animate using transition_states()
The first step is to create the object-to-be-animated
p_load(gifski)
anim <- ggplot(data, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the viz inside an html file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
Option 2 Animate using transition_time()
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(data, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Q5. Can you add a title to one or both of the animations above that will change in sync with the animation? [hint: search labeling for transition_states() and transition_time() functions respectively]
For the “anim” animation, I add labs(title = “{closest_state}”), which gives the animation a title that changes according to the year. This looks like this:
anim + transition_states(year,
transition_length = 1,
state_length = 1) +
labs(title = "{closest_state}")
For the second animation, I add labs(title = ‘Year: {frame_time}’), which does the same. This looks like this:
anim2 <- ggplot(data, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
transition_time(year) +
labs(title = 'Year: {frame_time}')
anim2
Q6. Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.[hint:search disabling scientific notation]
To do this, I add the layer scale_x_continuous() in which I can both make the logarithmic transformation of the x-axis and tell it to not use the scientific notation.
p_load(scales)
anim2 <- ggplot(data, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
transition_time(year) +
labs(title = 'Year: {frame_time}', x = "Gdp per capita", y = "Life expectancy", size = "Population") +
scale_x_continuous(trans = "log10", labels = function(x) format(x, scientific = FALSE)) +
scale_size_continuous(labels = function(x) format(x, scientific = FALSE))
anim2
Q7. Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]
I’m asking the following question: Which country’s population is larger, El Salvador’s or Denmark’s, and which year were the populations the same?
To answer the question, I make a plot where I first subset the data, so that it only takes the data from Denmark and El Salvador. On the x-axis I put “year” and on the y-axis “population”. I use geom_line() to get a smooth line to show the growth of each country, and color them by country, so that I get a legend telling me which line refers to which country. I use the function scale_y_continuous() to show the population size in whole numbers and not scientific notation. Finally, I label the axes and graph with labs().
ggplot(subset(data, country == "Denmark" | country == "El Salvador") , aes(year, pop)) +
geom_line(aes(color = country)) +
scale_y_continuous(labels = function(y) format(y, scientific = FALSE)) +
labs(title = "Population size of El Salvador and Denmark",
x = "Year",
y = "Population size")
Based on the graph, I can see that El Salvador currently has the largest popilation of the two countries, and that the population sizes were the same around year 1991.